Team of Prof.Huiyun Li, the Director of Center for Automotive Electronics,ShenzhenInstitutes of Advanced Technology, used multi-source data fusion algorithm in automatic driving perception decision control. Based on deep learning and prior knowledge, the team conducts multi-source sensing information environmental sensing technology in bad weather. The team also developed deep neural network chips and proposed a processor architecture based on coarse-grained reconfigurable neural morphic array architecture. In the parking and braking system, a flexible rotation around the longitudinal axis, lightweight structure, flexible before and after installation wire control system is developed. The team won the third prize of the national front vehicle detection project offline test of basic visual cognition ability of complex and dynamic traffic scene in 2018 China smart car future challenge.

Yang L, Li H. Vehicle-to-vehicle communication based on a peer-to-peer network with graph theory and consensus algorithm. IET Intelligent Transport Systems, 2018. doi: 10. 1049/iet-its. 2018. 5014

2019-07-04

      本实验室研发团队开发的自动驾驶车辆已通过中国多部委联合发布的《智能网联汽车道路测试管理规范（试行）》各项自动驾驶道路测试指标，已完成第三方1000公里封闭测试，获得深圳市交通运输局批准的开放道路测试许可。取得包括国家软件评测中心报告在内的五项第三方测试报告。后续本实验室开发的自动驾驶车辆可在深圳市交委划定的19个市内公开区域中进行智能网联汽车公开道路测试。同时，道路测试中将采集代表性场景的道路测试数据，用于台架测试系统的核心功能升级，提升台架测试系统对实际道路环境模拟的准确度。

2021-05-13

       12月8日，中国科学院深圳先进技术研究院与深圳市中科华劢科技有限公司共同宣布成立“智能汽车电子测试验证及系统仿真联合实验室”（以下简称联合实验室），并举行揭牌仪式。

深圳先进院院长特别助理、发展处处长毕亚雷，深圳先进院集成所副所长、汽车电子中心执行主任李慧云，深圳先进院发展处副处长黄小华，深圳市中科华劢科技有限公司董事长李磊，深圳市中科华劢科技有限公司副总经理穆范全等出席仪式。

       仪式上，毕亚雷致辞表示，联合实验室是深圳先进院成果转化与产业协同创新的重要平台，此次与深圳市中科华劢科技有限公司成立联合实验室，将有利于双方在车规级芯片及传感器等电子元器件领域进行科学前沿的探索、有助于双方在新产品开发及产业协同创新方面进行深入的合作。

       此外，毕亚雷表示，李磊董事长曾任职于中国科学院深圳先进技术研究院，这次是作为优秀院友回来院内谈合作发展，这也是先进院培养及对外输送人才，院友事业有成后反哺先进院的一个成功例子。毕亚雷表示，希望未来先进院能继续培养出更多人才，也欢迎这些院友们时时“回家”看看，保持联系与合作。

      李磊在致辞中感谢深圳先进院给予的技术指导，并表示自己过去也是深圳先进院的院友，从先进院得到了不少的帮助。现在自己出来创业成立公司后，也经常回想起在先进院学习工作的日子，感恩院内曾经给予过的支持及资源，因此即使现在离开了先进院，也希望能与先进院继续保持联系，建立长期合作。

       联合实验室项目负责人李慧云致辞表示，先进院汽车电子中心致力于新能源汽车整车控制集成、动力学、车联网及智能驾驶技术等前沿领域研究，通过此次的合作，双方能在车规级芯片领域进行进一步的科学探索。

       智能汽车电子测试验证及系统仿真联合实验室将主要围绕车规级芯片、传感器等电子元器件的测试验证，以及系统级仿真验证与测试等方面的前沿技术研究、新产品开发、技术平台建立及人才培养等多层面进行广泛合作，包括博士后培养，同时还包括双方共同申请各级科技资助计划项目等。

2021-12-09

汽车电子中心崔允端博士提出了利用贝叶斯滤波器将环境建模从马尔科夫决策过程(POMDP)转换至部分可观察马尔科夫决策过程(POMDP)的概率模型强化学习无人船控制算法，有效提升了各种海况下概率模型描述和应对环境不确定性的能力，减少自主学习无人船的平均控制误差40%以上。

该成果发表：Yunduan Cui, Lei Peng, and Huiyun Li.“Filtered Probabilistic Model Predictive Control-based Reinforcement Learning for Unmanned Surface Vehicles,” IEEE Transactions on Industrial Informatics, 2022, 18(10):6950-6961. (中科院一区TOP, IF=11.648). 

2022-09-08

The IEEE IntelligentVehicle Symposium is one of the comprehensive conferences organized by the IEEEIntelligent Transport Association. It aims to hold a seminar of the mostadvanced technology for researchers, engineers, and scholars around the world.It is the world’s top conference with the highest level, largest scale, andlongest history on  Intelligent vehicles.The 29th International Intelligent vehicle Conference was held in Changshu,China from June 26th to 30th, 2018. This year, the IEEE IV Symposium received603 papers from 34 countries and accepted 347 papers. The admission rate was57%.

Recently, two paperswere accepted by IV 2018, which demonstrate the research results from team of Prof.Huiyun Li, the Director of Center for Automotive Electronics, ShenzhenInstitutes of Advanced Technology, Chinese Academy of Sciences.

M.Sc.Zhiheng, Yang, the first author of the paper “Real-time Pedestrian and VehicleDetection for Autonomous Driving”, presented a pedestrian and vehicle detectionalgorithm to optimize feature extraction based on YOLOv2. This research takesadvantage of the priori experience on the sizes of feature boxes while performsstatistical analysis on the datasets with the labels of pedestrians andvehicles. Furthermore, the designed the initial value of the pre-selection boxis more representable for the characteristics of pedestrian and vehicle.Together with conventional training techniques for deep learning, this methodoptimizes the pedestrian and vehicle detection. The proposed algorithm not onlyimproves the detection accuracy but also maintains good detection efficiency.The experiments on KITTI dataset verify that the optimization algorithmsatisfies the real-time constraint as well as the accuracy of low-speedautonomous driving.

Ms.Jie Zou，as the first author ofImproved Sliding-Mode On-line Adaptive Position Control for AMT Clutch SystemsBased on Neural Networks, designed an improved Sliding-Mode position controllerbased on Neural Networks. The parameters of controller are tuned on-lineadaptively to control the clutch system to get better tracking performanceduring the vehicle starting. The clutch controller is embedded into a Modelica®based vehicle model and verified through the Model-in-the-Loop(MiL) simulation.The simulation results demonstrate that the controller has better trackingperformance and stronger robustness compared to the conventional controller,the parameters of which are tuned off-line via trial-and-error. That istime-consuming and often yield poor results. The method proposed in this paperenhances the generalization ability of the controller.

In addition, the IEEEIV2018 workshop entitled Intelligent Transportation and AutonomousVehicles hosted by Researcher Huiyun Li was successfully held inChangshu, China on June 26th, 2018.

2018-09-14

Presents a framework capable of providing active short-term navigation, combining the intelligence of active assistance with the freedom of location independence. Demonstration data from an able expert while driving the mobility aid in a standard indoor setting is used off-line to learn reference behavioral models of navigation given perceptual information from the platform surroundings and the input controls exerted by the user while navigating. These serve as the foundation for on-line probabilistic short-term destination inference using the instantaneously available data from the user and on-board sensors. This is coupled with a real-time stochastic optimal path generation able to exploit the same short term demonstration paths from the expert with the belief they capture both the drivers awareness of the platforms physical geometry and appropriate behaviors for their surroundings.

2021-05-12

2021-11-12